Field of the Invention
The present invention relates to healthcare fraud preemption, and more particularly to real-time computer systems and software products connected to medical diagnosis, procedure coding, insurance, and billing systems, and programmed to detect and preempt abuse, fraud, and excessive profits by medical insiders and institutions.
Background
Fraud, in all its forms can be: (a) never caught, (b) caught early, (c) caught late, or (d) prevented altogether. HealthCare fraud has blossomed in recent years because deep pockets like the Government and large insurance companies are now more than ever paying all the bills. That removed the frontline of patients who knew what billings were legitimate and reasonable. The purchasing decisions have been taken away from the purchasers, and they no longer review and approve medical billings. So the new environment makes the rewards of fraud large and the risks of being exposed and punished small.
The once inherent prerogatives of patients to choose doctors, procedures, finance costs, demand explanations, make payments, and even review the totals or get secondary opinions or competitive bids has now been completely requisitioned by the Insurance Companies and Government. As a result, doctors themselves and other first line healthcare providers are incapable of telling patients what the billing costs will be for any of their prescribed drugs or services, there's no reason for them to care or know. All that matters is that “it's covered by your insurance.” When told what the costs and consequences are, doctors are often surprised if they're not themselves on the receiving end of those payments.
Insurance Companies and Government, of course, try to control fraud, but their third party, after the fact status to the treatments makes them less able and less effective in controlling medical fraud. Hospitals, clinics, pharmaceutical companies, and other healthcare providers in general have stepped in to capitalize on these shortcomings. Costs, as a direct result have spiraled beyond all reason.
The visibility and insight patients once had fifty years ago acted as a self-limiting and self-correcting mechanism that keep costs low and under control. Patients were uniquely able to negotiate less expensive alternatives in real time, and were motivated to do so because it was they that were paying out of their own pockets.
The computer automation that's starting to take root in the healthcare industry offers some salvation from this disorder. Computer nodes all along the data processing and payment chain can be employed to do cross-checking, sanity tests, behavioral analysis, comprehensive monitoring of single healthcare providers, large database data mining, and even more exotic fraud detection, prevention, and correction.
Medical treatments, procedures, and medicines are expensive and a lot of money flows through the hands that provide them. Some of those hands are not entirely honest or forthright. Others are in the business just to cheat it at every opportunity. The insurance companies and government agencies that process, approve, and payout on medical billings are too distant, too remote, too detached, and too preoccupied to be very good at recognizing when they are being hoodwinked and cheated. Law enforcement is difficult, and crimes related to medical fraud largely go undetected and uncontrolled.
Medicine in America has changed radically from private doctors who did house calls and billed the patients in cash on-the-spot, to anonymous clinics that process thousands of medically insured patients who never see what their doctors or clinics are billing their insurers. These modern patients are completely prevented and disallowed from shopping for treatments and diagnosis, and all the old competition and cross-checking it produced have evaporated. Nothing is left to expose or identify fraud because the billings run open-loop to third parties with no independent methods of verification to rely on.
More and more medicine in America is moving to electronic records, billings, and payments. This then provides an automated means for data to be collected and analyzed. Such collection can occur even before the underlying procedures get paid for by the government agencies or insurance companies. A diagnosis of real-time fraud can be used as an alert to catch the fraudsters red handed and before they get their hands on the money.
CHARLES PIPER identified in a January/February 2013 Article he published in Fraud Magazine:
TEN COMMON HEALTH CARE HEALTHCAREPROVIDER FRAUD SCHEMES1.Billing for services not rendered.2.Billing for a non-covered service as a covered service.3.Misrepresenting dates of service.4.Misrepresenting locations of service.5.Misrepresenting healthcare provider of service.6.Waiving of deductibles and co-payments.7.Incorrect reporting of diagnoses or procedures (includes unbundling).8.Overutilization of services.9.Corruption (kickbacks and bribery).10.False or unnecessary issuance of prescription drugs.
Conventional defenses to healthcare fraud have had mediocre results, they often waste and abuse space, and have little scalability. Most require high manual effort.
What can be seen, and what fraud and abuse-detection systems report, is never the real problem. A key characteristic of most white-collar fraud and abuse cases is that unless they are detected close to the time they are committed, they will probably remain undetected forever. Certainly the money absconded will never come back. And the way things work in America, only the Government, Lawyers, and Insurance Companies will benefit many years later from any kind of fines, penalties, or restitution that gets imposed in a Consent Order. The patients who were abused will never see any of it.
Fraud and abuse tend to thrive when aggressive prevention procedures are not in place. Healthcare fraud and abuse control is a perpetual, never-ending game, not one that can be won and held. fraud and abuse controls target criminals who distinguish themselves by their great ingenuity. They continuously adjust and adapt their techniques, and blossom on concocting intricate new rip-off schemes.
A static set of “filters” therefore will have only short-term value. Conventional answers and responses to fraud and abuse can never deliver real prevention because they must first be aware of the fraud and abuse before fraud and abuse can be detected, so they are unable to detect new types of fraud and abuse as they occur. Furthermore, they require massive amounts of historical data to recognize patterns and the quality of the system depends on the quality of historical data gathered. All fraud and abuse possibilities must be coded and lack adaptation to new types of fraud and abuse. Therefore, like current virus programs, current fraud and abuse technologies are outdated as soon as they are released. This is clearly inadequate in a world of ever-more clever thieves.
There are critical characteristics of fraud and abuse, such as unpredictability, exponential growth and more sophisticated and advanced fraud and abuse techniques, that, when addressed by the proper technology, can be successfully curtailed. However, current systems employ methods that do not respond to these changes.
Computer scientists initially created programs called fraud and abuse Scanners that detected known cases of fraud and abuse by using a profile or signature that uniquely identified these instances of fraud and abuse. These signatures were cataloged and stored in a database as part of the anti-fraud and abuse program. For each transaction, the anti-fraud and abuse program scanned and compared them to the known signatures stored in the database. If a match occurred, the file was determined to be fraudulent.
This method, called Known-fraud and abuse Scanning, proved useful during the first few years of the fraud and abuse scourge, but today it is completely ineffective. The problem is that keeping the database current requires updating the database as soon as new fraudulent activity is discovered.
With new fraud and abuse appearing each new day, company employees would have to update their computer's anti-fraud and abuse databases just as often (and only in the event that they receive a warning about it before it costs their company).
The best of the conventional fraud and abuse detection systems still use Neural Network back propagation (BP) algorithms. BP Neural Networks learn the patterns in the relationships between inputs and outputs, and then are able to respond intelligently to new inputs, e.g., using the experiences gained during training. BP networks belong to a supervised-learning class of Neural Networks. The error signals they produce during training are used to supervise the learning process. Neural Networks that use supervised learning techniques have proven themselves in classification, generalization and prediction and many other practical applications. But, needing to know what output is desired for each input before any training begins can be very limiting.
When the desired outputs are unknown during training for less than all input patterns, new incidences of fraud and abuse may not be detected in real-time. There is a crucial lag between detection and infection (fraud and abuse arrival).
Neural Networks, statistical modeling or profiling have been applied to fraud and abuse detection. But for them to be effective, they need a large database of cases in which fraud and abuse were detected. However, for this to work later the fraudulent methods and abuse must not have changed much. Such tools are impotent when the fraud and abuse either too closely resembles normal activity, or if it constantly shifts as the fraudsters adapt to changing surveillance strategies and technologies. (Which, by the way, is what the influenza virus does naturally.)
In Intelligent Fraud prevention the “leakage” in the industry is the problem. Illegal activity, while significant in absolute numbers, is trivial when compared to a $2.8 trillion in annual healthcare spending. The solutions provided must address the breadth of the leakage. For example, simple excessive billing of preventive visits (Evaluation and Management claims) results in $20-$30 inflated billing per visit. With one billion primary care physician visits each year, that leakage alone exceeds the entire fraud recoveries in the industry in a single year.
Conventional analytic solutions, even those that claim to be non-hypothesis based, still operate within very rigid boundaries. They are either designed or tuned to look at various scenarios in such a way that they will only catch a limited range of the leakage problem. When something truly surprising happens, or a variation occurs that was not anticipated, systems based on such models fail to perform.
Modern systems need to be sophisticated, unsupervised, learn as they go, and conduct peer reviews based treatment, zip code, etc.
Conventional fraud and abuse detection systems are like Virus Tools, they must be instructed and supplied with templates to detect fraud and abuse. These the expense of pioneers who were hit first and early. This is clearly laughable in a world of ever-more clever thieves.
New behaviors of fraud and abuse arise daily.
Conventional solutions to healthcare fraud, waste and abuse space have obtained only mediocre results. They lack scalability and always require high manual effort. We can do better.